Reinvent 4: Modern AI–driven generative molecule design

REINVENT 4 is a modern open-source generative AI framework for the design of small molecules. The software utilizes recurrent neural networks and transformer architectures to drive molecule generation. These generators are seamlessly embedded within the general machine learning optimization algorithms, transfer learning, reinforcement learning and curriculum learning. REINVENT 4 enables and facilitates de novo design, R-group replacement, library design, linker design, scaffold hopping and molecule optimization. This contribution gives an overview of the software and describes its design. Algorithms and their applications are discussed in detail. REINVENT 4 is a command line tool which reads a user configuration in either TOML or JSON format. The aim of this release is to provide reference implementations for some of the most common algorithms in AI based molecule generation. An additional goal with the release is to create a framework for education and future innovation in AI based molecular design. The software is available from https://github.com/MolecularAI/REINVENT4 and released under the permissive Apache 2.0 license. Scientific contribution. The software provides an open–source reference implementation for generative molecular design where the software is also being used in production to support in–house drug discovery projects. The publication of the most common machine learning algorithms in one code and full documentation thereof will increase transparency of AI and foster innovation, collaboration and education. Supplementary Information The online version contains supplementary material available at 10.1186/s13321-024-00812-5.


Effect of Inception and Scaffold Diversity Filter
Figure S1 summarizes how inception and scaffold diversity filter (DF) affect Reinforcement Learning (RL).All results are taken as the average over 5 independent runs.The highlighted areas denote the standard deviation for each data point.
The data (orange and blue lines)in S1a show how inception leads to finding significantly more compounds with a smaller binding free energy.When inception is switched on, DF displays higher ∆G values.With DF on RL is forced to create more diverse structures and does not allow the run to exploit a smaller number of scaffolds.
Subfigure S1b displays the fraction of valid SMILES found in each RL step.When DF is on the number of valid SMILES drops relative to when DF is off.
In S1c we see the effect of inception on the number of unique scaffolds found (DF is on).Inception leads to a larger diversity in sampled structures.
Subfigure S1d shows how inception affects how many unique scaffolds are found more than N times.N is the size of the DF memory and was taken as 10 in this case.Clearly, with inception RL samples larger numbers of scaffolds in line with S1c.
The scoring function used for this examples is composed of QED, custom alerts, number of stereocentres (set to zero) and binding free energy.The latter is built with a QSAR-like response model derived from about 10 000 MM-PBSA calculations of ligands bound to the main protease of SARS-2.The data here only serves as a demonstration.Detailed results will be published elsewhere.The batch size was set to 100, the RL learning strategy was DAP with σ = 128 and a learning rate of 0.0001, DF was of type Murcko scaffold with a memory size of 10 and minimum score threshold for storage of 0.4 and the inception memory size was set to 50 with a sample size of 10.RL was executed for 300 steps.

Fig. S1 :##
Fig. S1: Effect of inception and scaffold diversity filter.a) Average binding free energy, b) Fraction of valid SMILES found per batch (typically above 95%), c) Number of unique scaffolds, d) Number of scaffolds found more than 10 times.